CN115454176A - Wisdom green house ventilation control system based on thing networking - Google Patents
Wisdom green house ventilation control system based on thing networking Download PDFInfo
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- CN115454176A CN115454176A CN202211079467.0A CN202211079467A CN115454176A CN 115454176 A CN115454176 A CN 115454176A CN 202211079467 A CN202211079467 A CN 202211079467A CN 115454176 A CN115454176 A CN 115454176A
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- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
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- Y02A40/00—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
- Y02A40/10—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production in agriculture
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Abstract
The invention discloses an intelligent agricultural greenhouse ventilation control system based on the Internet of things, relates to the technical field of intelligent agriculture, and solves the technical problems that the influence degree of the internal and external environments of a greenhouse on crops is not comprehensively considered in the prior art, so that the ventilation control of the greenhouse is not accurate enough, and the healthy growth of the crops is influenced; according to the method, the ventilation evaluation coefficient is obtained by combining external environment data, and the set period suitable for ventilation is determined based on the ventilation evaluation coefficient; the method comprises the following steps of performing joint evaluation on internal environment data and external environment data, judging whether intelligent ventilation can be performed or not, and effectively avoiding the influence of external severe environment on the growth of crops; according to the method, whether intelligent ventilation is performed or not is determined according to the ventilation evaluation coefficient of the greenhouse, so that the influence of the external severe environment on internal crops during ventilation can be avoided; and real-time feedback can be carried out according to the size of the ventilation evaluation coefficient, so that the ventilation duration and the ventilation intensity are controlled.
Description
Technical Field
The invention belongs to the field of intelligent agriculture, relates to an intelligent agricultural greenhouse ventilation control technology based on the Internet of things, and particularly relates to an intelligent agricultural greenhouse ventilation control system based on the Internet of things.
Background
The temperature in the greenhouse can rise along with the rise of the external temperature, and when the weather is clear, the temperature in the greenhouse is 6-15 ℃ higher than the external temperature. Therefore, the greenhouse needs to be ventilated frequently in the crop planting process, the temperature in the greenhouse is suitable for crop growth, and redundant carbon dioxide is discharged.
The prior art (patent application publication No. CN 111165221A) discloses a greenhouse intelligent ventilation facility based on meteorological information and a control method thereof, which compare, analyze and judge collected meteorological parameter values with set limit values, and further realize intelligent control of ventilation. In the prior art, in the process of controlling the ventilation of the greenhouse, only the internal environment and the external environment of the greenhouse are compared, and when the comparison result meets the ventilation condition, the ventilation control is immediately performed, so that the influence of the environment on crops in the greenhouse is not comprehensively considered, the ventilation control of the greenhouse is not accurate enough, and the healthy growth of the crops is influenced; therefore, an intelligent agricultural greenhouse ventilation control system based on the internet of things is urgently needed.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art; therefore, the invention provides an intelligent agricultural greenhouse ventilation control system based on the Internet of things, which is used for solving the technical problems that the influence degree of the environment inside and outside a greenhouse on crops is not comprehensively considered in the prior art, so that the ventilation control of the greenhouse is not accurate enough, and the healthy growth of the crops is influenced.
In order to achieve the above purpose, the invention provides a smart agricultural greenhouse ventilation control system based on the internet of things, which comprises a central analysis module, and a data acquisition module, an intelligent terminal and an execution control module which are connected with the central analysis module, wherein the data acquisition module is connected with various types of data sensors or databases;
a data acquisition module: in the intelligent ventilation stage, acquiring internal environment data of the greenhouse by using a data sensor, and extracting external environment data of the greenhouse by using a database;
a central analysis module: analyzing the internal environment data by combining with the crop type to determine whether the internal environment data accords with the crop growth condition; if yes, no processing is carried out, and if not, external environment data are obtained and analyzed; and
when the internal environment data do not accord with the crop growth condition, the influence degree of the internal environment data and the external environment data on the crop growth is analyzed, and the execution control module carries out intelligent ventilation according to the influence degree.
Preferably, the central analysis module is respectively in communication and/or electrical connection with the data acquisition module, the intelligent terminal and the execution control module; the intelligent terminal comprises a mobile phone and a computer;
the data acquisition module is in communication and/or electrical connection with a data sensor or a database; the execution control module is used for controlling the intelligent ventilation equipment to perform intelligent ventilation.
Preferably, the staff passes through intelligent terminal sets up the ventilation mode, when being in intelligent ventilation stage, then data acquisition module automatic acquisition warmhouse booth's internal environment data includes:
when the ventilation mode is in the intelligent ventilation stage, activating data sensors uniformly distributed in the greenhouse; wherein the data sensor comprises a temperature sensor, a humidity sensor and a carbon dioxide sensor;
and acquiring original data through the data sensor, aligning the original data according to the acquisition time, and generating the internal environment data.
Preferably, the central analysis module analyzes whether the internal environment data meets the crop growth condition, including:
extracting internal environment elements in the internal environment data, and marking the internal environment elements as NHYi; and obtaining an element deviation coefficient YPXi by a formula YPXi = | NHYi-NHBi |; wherein i is a positive integer, and NHBi is the optimal value corresponding to NHYi;
when the element deviation coefficient YPxi is more than or equal to YPYi, judging that the internal environment element NHYi does not accord with the crop growth condition; where YPYi is the deviation threshold of the element corresponding to NHYi.
Preferably, when any internal environment element in the internal environment data does not accord with the growth condition of the crops, the external environment data is collected through the data collection module; and
acquiring external environment data from a database, wherein the external environment data comprises current environment data and predicted environment data; the forecasting environment data are obtained through a third-party meteorological platform and stored in a database.
Preferably, when the internal environment data do not accord with the crop growth conditions, the central analysis module respectively analyzes the influence degree of the internal environment data and the influence degree of the external environment data on the crops; and
and acquiring a ventilation evaluation coefficient based on the influence degree of the internal and external environment data on the crops, and determining the intelligent ventilation intensity and the intelligent ventilation time according to the comparison result of the ventilation evaluation coefficient and the ventilation evaluation threshold.
Preferably, the hub analysis module obtains the ventilation assessment coefficient based on the internal environment data and the external environment data, and includes:
extracting external environment elements in the external environment data, and marking as WHYi; when the numbers i of the internal environment element and the external environment element are consistent, the internal environment element and the external environment element represent the same environment element;
obtaining a ventilation evaluation coefficient TPX through a formula TPX = alpha 1 x WHY1-NHY1 + alpha 2 x WHY2-NHY2 + 8230, + alpha i x WHYi-NHYi |; where α i is a weight set empirically.
Preferably, when the ventilation evaluation coefficient is calculated, the external environment is divided according to a set period, and the corresponding ventilation evaluation coefficient is obtained; and
and when the ventilation evaluation coefficient of the current set period is greater than the ventilation evaluation threshold value, judging that the current set period is not suitable for intelligent ventilation, and calculating the ventilation evaluation coefficient of the next set period.
Compared with the prior art, the invention has the beneficial effects that:
1. the method comprises the steps of collecting internal environment data and external environment data of the greenhouse, analyzing and judging whether the internal environment data are abnormal or not, acquiring a ventilation evaluation coefficient by combining the external environment data if the internal environment data are abnormal, determining whether a corresponding set period is suitable for ventilation or not based on the ventilation evaluation coefficient, and controlling intelligent ventilation equipment to perform intelligent ventilation by an execution control module if the corresponding set period is suitable for ventilation; carry out joint assessment to internal environment data and external environment data, judge whether can carry out intelligent ventilation, effectively avoid outside adverse circumstances to the influence of crops growth.
2. According to the method, whether intelligent ventilation is performed or not is determined according to the influence degree of the internal environment data and the external environment data of the greenhouse on the growth of crops, namely the ventilation evaluation coefficient, so that the influence of the external severe environment on the internal crops during ventilation can be avoided; and real-time feedback can be carried out according to the ventilation evaluation coefficient, so that the ventilation time and the ventilation strength are controlled.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of the working steps of the present invention;
fig. 2 is a schematic diagram of the system of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-2, in a first aspect of the present invention, an embodiment of the present invention provides an intelligent agricultural greenhouse ventilation control system based on the internet of things, including a central analysis module, and a data acquisition module, an intelligent terminal and an execution control module connected thereto, where the data acquisition module is connected to various types of data sensors or databases;
a data acquisition module: in the intelligent ventilation stage, acquiring internal environment data of the greenhouse by a data sensor, and extracting external environment data of the greenhouse by a database;
a pivot analysis module: analyzing the internal environment data by combining with the crop type to determine whether the internal environment data accords with the crop growth condition; if yes, no processing is carried out, and if not, external environment data are obtained and analyzed; and when the internal environment data do not accord with the crop growth conditions, analyzing the influence degree of the internal environment data and the external environment data on the crop growth, and executing the control module to perform intelligent ventilation according to the influence degree.
In the prior art, when the ventilation of the greenhouse is controlled, the ventilation is started when the environment inside the greenhouse is detected not to accord with the growth condition of crops, and the external environment of the greenhouse is not detected and evaluated; in case the external environment is comparatively abominable, prior art's ventilation control mode can lead to warmhouse booth internal environment abrupt change, and is unfavorable to crops growth.
The method comprises the steps of collecting internal environment data and external environment data of the greenhouse, analyzing and judging whether the internal environment data are abnormal or not, if so, acquiring a ventilation evaluation coefficient by combining the external environment data, determining whether a corresponding set period is suitable for ventilation or not based on the ventilation evaluation coefficient, and if so, controlling intelligent ventilation equipment to perform intelligent ventilation through an execution control module; carry out joint assessment to internal environment data and external environment data, judge whether can carry out intelligent ventilation, effectively avoid outside adverse circumstances to the influence of crops growth.
The central analysis module is respectively communicated and/or electrically connected with the data acquisition module, the intelligent terminal and the execution control module; the data acquisition module is in communication and/or electrical connection with the data sensor or the database; and the execution control module is used for controlling the intelligent ventilation equipment to perform intelligent ventilation.
The central analysis module is used for data processing and controlling equipment connected with the central analysis module, and mainly performs data interaction with the data acquisition module, the intelligent terminal and the execution control module; the intelligent terminal is mainly used for setting a ventilation mode and displaying an intelligent ventilation state in real time; the data acquisition module is mainly used for acquiring data according to the processing progress of the central analysis module and carrying out data interaction with various types of data sensors or databases. The data sensor mainly is temperature sensor, humidity transducer and the gas sensor (mainly for the carbon dioxide sensor) that sets up inside warmhouse booth or outside, when not setting up the data sensor outside warmhouse booth, then can draw external environment data and store in the database through third party meteorological platform. It should be noted that the intelligent ventilation device mainly includes an automatic film rolling device, a ventilation fan, a ventilation window, and the like.
In the application, a worker sets a ventilation mode through an intelligent terminal, and when the intelligent ventilation stage is in, a data acquisition module automatically acquires internal environment data of the greenhouse, and the method comprises the following steps:
when the ventilation mode is in the intelligent ventilation stage, activating data sensors uniformly distributed in the greenhouse; and acquiring original data through a data sensor, and aligning the original data according to the acquisition time to generate internal environment data.
The ventilation mode of the greenhouse comprises an intelligent ventilation stage, a manual ventilation stage and the like, and the intelligent ventilation mode refers to that the ventilation control of the greenhouse is controlled by a central analysis module. And in the intelligent ventilation stage, the data sensor is automatically activated, and the original data is collected by the data sensor in real time. Theoretically, the acquired raw data comprises temperature, humidity, carbon dioxide concentration and the like, but the acquisition time of each data sensor is not consistent, the acquisition time of the temperature, the humidity, the carbon dioxide concentration and the like in the raw data is disordered, and in order to ensure the accuracy of the analysis of the internal environment, the data are aligned according to the acquisition time, and then the internal environment data are generated.
The invention discloses a central analysis module for analyzing whether internal environment data meet crop growth conditions, which comprises the following steps:
extracting internal environment elements in the internal environment data, and marking the internal environment elements as NHYi; and obtaining a factor deviation coefficient YPXi by a formula YPXi = | NHYi-NHBi |; when the element deviation coefficient YPxi is not less than YPYi, the internal environment element NHYi is judged not to meet the crop growth condition.
i is a positive integer and mainly numbers environmental elements, such as temperature labeled 1, humidity labeled 2, and carbon dioxide concentration labeled 3.NHBi is an optimal value corresponding to NHYi, and when i =1, NHB1 represents the most appropriate temperature value in the greenhouse corresponding to the crop. YPYi is an element deviation threshold corresponding to NHYi and is empirically set.
Assuming that the internal temperature of the greenhouse is 30 ℃, the corresponding optimal growth temperature of crops is 25 ℃, and the temperature deviation threshold (element deviation threshold) is 3 ℃; the temperature inside the greenhouse is not suitable for the growth of crops, and the greenhouse theoretically needs to be ventilated.
According to the method, when any internal environment element in the internal environment data does not accord with the growth condition of crops, the external environment data is collected through the data collection module; acquiring external environment data from a database, wherein the external environment data comprises current environment data and predicted environment data; the forecasting environment data is acquired through a third-party meteorological platform and stored in a database.
When any internal environment element in the internal environment data, such as any one of temperature, humidity and carbon dioxide concentration is abnormal, the ventilation processing is judged to be needed. But whether the external environmental data meets the ventilation requirements requires further analysis. Therefore, the external environment data is acquired through the data acquisition module, and the external environment data not only includes the current environment data for judging whether the current external environment meets the ventilation requirement, but also includes the predicted environment data for judging which set period meets the ventilation requirement in the future.
In the application of the invention, when the internal environment data do not accord with the growth conditions of crops, the central pivot analysis module respectively analyzes the influence degree of the internal environment data and the external environment data on the crops; and acquiring a ventilation evaluation coefficient based on the influence degree of the internal and external environment data on the crops, and determining the intelligent ventilation intensity and the intelligent ventilation time according to the comparison result of the ventilation evaluation coefficient and the ventilation evaluation threshold value.
When determining whether ventilation is needed, the influence degree of the internal environment of the greenhouse on crops needs to be comprehensively considered, and when the influence degree of the internal environment data on the crop growth is greater than that of the external environment data, ventilation is needed; when the external environmental data affects the growth of the crops to a degree greater than or equal to the internal environmental data, ventilation should not be performed.
The invention discloses a central analysis module for acquiring a ventilation evaluation coefficient based on internal environment data and external environment data, which comprises the following steps:
extracting external environment elements in the external environment data, and marking as WHYi; the ventilation evaluation coefficient TPX is obtained by the formula TPX = α 1 × | wy 1-NHY1| + α 2 × | wy 2-NHY2| + \8230, + α i × | WHYi-NHYi |.
When the numbers i of the internal environment element and the external environment element are identical, they represent the same environment element, i.e., WHYi and NHY1 may both represent temperature values, only one represents an external temperature value and one represents an internal temperature value. And alpha i is a weight set according to experience, and the weight values of the temperature, the humidity and the carbon dioxide concentration are set according to actual experience. The method has the main idea that on the basis that the change of internal environment data is not excessively deviated, the difference value of each internal environment element and each external environment element is calculated to determine a ventilation evaluation coefficient; the internal environment data generally does not deviate too much from the optimal value in the smart tuning state, such as when | WHY1-NHY1| is larger, it indicates that the external temperature data deviates more from the optimal temperature.
In other preferred embodiments, a multi-step determination may be performed, and when the difference between the internal environment element and the external environment element is too large and the internal environment element is closer to the optimal value, it indicates that the external environment element may adversely affect the growth of crops after ventilation, and thus is not suitable for ventilation.
According to the method, when the ventilation evaluation coefficient is calculated, the external environment is divided according to a set period, and the corresponding ventilation evaluation coefficient is obtained; and when the ventilation evaluation coefficient of the current set period is greater than the ventilation evaluation threshold, judging that the current set period is not suitable for intelligent ventilation, and calculating the ventilation evaluation coefficient of the next set period.
When the current set period is not suitable for ventilation operation, the environment data is predicted to judge which set period is suitable for ventilation operation in the future, and intelligent ventilation can be controlled through timing processing. The ventilation duration and the ventilation intensity can be adjusted by real-time feedback.
Part of data in the formula is obtained by removing dimension and taking the value to calculate, and the formula is obtained by simulating a large amount of collected data through software and is closest to a real situation; the preset parameters and the preset threshold values in the formula are set by those skilled in the art according to actual conditions or obtained through simulation of a large amount of data.
The working principle of the invention is as follows:
in the intelligent ventilation stage, collecting internal environment data of the greenhouse through a data collection module; the central analysis module analyzes the internal environmental data in combination with the crop type to determine whether an anomaly is required.
When the internal environment data are abnormal, the data acquisition module acquires the external environment data through the database; the central analysis module analyzes the influence degree of the regional environment data and the external environment data on the growth of the crops.
And the execution control module intelligently ventilates according to the influence degree of the internal and external environment data on the growth of crops, and adjusts the ventilation time and the ventilation intensity in real time according to the feedback result in the ventilation process.
Although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the scope of the present invention.
Claims (8)
1. The utility model provides a wisdom green house ventilation control system based on thing networking, includes maincenter analysis module to and the data acquisition module, intelligent terminal and the execution control module that are connected with it, data acquisition module is connected its characterized in that with each type data sensor or database:
a data acquisition module: in the intelligent ventilation stage, acquiring internal environment data of the greenhouse by a data sensor, and extracting external environment data of the greenhouse by a database;
a pivot analysis module: analyzing the internal environment data by combining with the crop type to determine whether the internal environment data accords with the crop growth condition; if yes, no processing is carried out, and if not, external environment data are obtained and analyzed; and
when the internal environment data do not accord with the crop growth condition, the influence degree of the internal environment data and the external environment data on the crop growth is analyzed, and the execution control module carries out intelligent ventilation according to the influence degree.
2. The intelligent agricultural greenhouse ventilation control system based on the internet of things of claim 1, wherein the central analysis module is respectively in communication and/or electrical connection with the data acquisition module, the intelligent terminal and the execution control module; the intelligent terminal comprises a mobile phone and a computer;
the data acquisition module is in communication and/or electrical connection with a data sensor or a database; and the execution control module is used for controlling the intelligent ventilation equipment to perform intelligent ventilation.
3. The intelligent agricultural greenhouse ventilation control system based on the internet of things as claimed in claim 1, wherein staff sets a ventilation mode through the intelligent terminal, and when in an intelligent ventilation stage, the data acquisition module automatically acquires internal environment data of the greenhouse, and the system comprises:
when the ventilation mode is in the intelligent ventilation stage, activating data sensors uniformly distributed in the greenhouse; wherein the data sensor comprises a temperature sensor, a humidity sensor and a carbon dioxide sensor;
and acquiring original data through the data sensor, aligning the original data according to the acquisition time, and generating the internal environment data.
4. The Internet of things-based intelligent agricultural greenhouse ventilation control system of claim 3, wherein the central analysis module analyzes whether the internal environment data meets the crop growth conditions or not, and comprises:
extracting internal environment elements in the internal environment data, and marking the internal environment elements as NHYi; and obtaining a factor deviation coefficient YPXi by a formula YPXi = | NHYi-NHBi |; wherein i is a positive integer, and NHBi is the optimal value corresponding to NHYi;
when the element deviation coefficient YPxi is more than or equal to YPYi, judging that the internal environment element NHYi does not accord with the crop growth condition; where YPYi is the deviation threshold of the element corresponding to NHYi.
5. The Internet of things-based intelligent agricultural greenhouse ventilation control system as claimed in claim 4, wherein when any internal environment element in the internal environment data does not accord with crop growth conditions, the external environment data is collected through the data collection module; and
acquiring external environment data from a database, wherein the external environment data comprises current environment data and predicted environment data; the forecasting environment data are obtained through a third-party meteorological platform and stored in a database.
6. The Internet of things-based intelligent agricultural greenhouse ventilation control system as claimed in claim 5, wherein the central analysis module is used for analyzing the influence degree of the internal environment data and the influence degree of the external environment data on crops respectively when the internal environment data do not meet the growth conditions of the crops; and
and acquiring a ventilation evaluation coefficient based on the influence degree of the internal and external environment data on the crops, and determining the intelligent ventilation intensity and the intelligent ventilation time according to the comparison result of the ventilation evaluation coefficient and the ventilation evaluation threshold.
7. The Internet of things-based smart agriculture greenhouse ventilation control system of claim 6, wherein the central analysis module obtains the ventilation evaluation coefficient based on internal environment data and external environment data, comprising:
extracting external environment elements in the external environment data, and marking as WHYi; when the numbers i of the internal environment element and the external environment element are consistent, the internal environment element and the external environment element represent the same environment element;
obtaining a ventilation evaluation coefficient TPX through a formula TPX = alpha 1 x WHY1-NHY1 + alpha 2 x WHY2-NHY2 + 8230, + alpha i x WHYi-NHYi |; where α i is a weight set empirically.
8. The system according to claim 7, wherein when the ventilation evaluation coefficients are calculated, the external environment is divided according to a set period, and the corresponding ventilation evaluation coefficients are obtained; and
and when the ventilation evaluation coefficient of the current set period is greater than the ventilation evaluation threshold value, judging that the current set period is not suitable for intelligent ventilation, and calculating the ventilation evaluation coefficient of the next set period.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202211079467.0A CN115454176A (en) | 2022-09-05 | 2022-09-05 | Wisdom green house ventilation control system based on thing networking |
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| CN202211079467.0A CN115454176A (en) | 2022-09-05 | 2022-09-05 | Wisdom green house ventilation control system based on thing networking |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116449898A (en) * | 2023-06-20 | 2023-07-18 | 江苏中盟电气设备有限公司 | Remote temperature and humidity control system for switch cabinet |
| CN117519354A (en) * | 2024-01-08 | 2024-02-06 | 山西省农业机械发展中心 | Intelligent information remote monitoring system for facility agriculture |
-
2022
- 2022-09-05 CN CN202211079467.0A patent/CN115454176A/en not_active Withdrawn
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116449898A (en) * | 2023-06-20 | 2023-07-18 | 江苏中盟电气设备有限公司 | Remote temperature and humidity control system for switch cabinet |
| CN116449898B (en) * | 2023-06-20 | 2023-09-22 | 江苏中盟电气设备有限公司 | Remote temperature and humidity control system for switch cabinet |
| CN117519354A (en) * | 2024-01-08 | 2024-02-06 | 山西省农业机械发展中心 | Intelligent information remote monitoring system for facility agriculture |
| CN117519354B (en) * | 2024-01-08 | 2024-03-29 | 山西省农业机械发展中心 | Intelligent information remote monitoring system for facility agriculture |
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Application publication date: 20221209 |